Superdiffusive Rotation of Interfacial Water on Noble Metal Surface.

Interfacial water on a metal surface acts as an active layer through the reorientation of water, thereby facilitating the energy transfer and chemical reaction across the metal surface in various physicochemical and industrial processes. However, how this active interfacial water collectively behaves on flat noble metal substrates remains largely unknown due to the experimental limitation in capturing librational vibrational motion of interfacial water and prohibitive computational costs at the first-principles level. Herein, by implementing a machine-learning approach to train neural network potentials, we enable performing advanced molecular dynamics simulations with ab initio accuracy at a nanosecond scale to map the distinct rotational motion of water molecules on a metal surface at room temperature. The vibrational density of states of the interfacial water with two-layer profiles reveals that the rotation and vibration of water within the strong adsorption layer on the metal surface behave as if the water molecules in the bulk ice, wherein the O-H stretching frequency is well consistent with the experimental results. Unexpectedly, the water molecules within the adjacent weak adsorption layer exhibit superdiffusive rotation, contrary to the conventional diffusive rotation of bulk water, while the vibrational motion maintains the characteristic of bulk water. The mechanism underlying this abnormal superdiffusive rotation is attributed to the translation-rotation decoupling of water, in which the translation is restrained by the strong hydrogen bonding within the bilayer interfacial water, whereas the rotation is accelerated freely by the asymmetric water environment. This superdiffusive rotation dynamics may elucidate the experimentally observed large fluctuation of the potential of zero charge on Pt and thereby the conventional Helmholtz layer model revised by including the contribution of interfacial water orientation. The surprising superdiffusive rotation of vicinal water next to noble metals will shed new light on the physicochemical processes and the activity of water molecules near metal electrodes or catalysts.

Dynamics of the charge transfer to solvent process in aqueous iodide.

Charge-transfer-to-solvent states in aqueous halides are ideal systems for studying the electron-transfer dynamics to the solvent involving a complex interplay between electronic excitation and solvent polarization. Despite extensive experimental investigations, a full picture of the charge-transfer-to-solvent dynamics has remained elusive. Here, we visualise the intricate interplay between the dynamics of the electron and the solvent polarization occurring in this process. Through the combined use of ab initio molecular dynamics and machine learning methods, we investigate the structure, dynamics and free energy as the excited electron evolves through the charge-transfer-to-solvent process, which we characterize as a sequence of states denoted charge-transfer-to-solvent, contact-pair, solvent-separated, and hydrated electron states, depending on the distance between the iodine and the excited electron. Our assignment of the charge-transfer-to-solvent states is supported by the good agreement between calculated and measured vertical binding energies. Our results reveal the charge transfer process in terms of the underlying atomic processes and mechanisms.

Evaluating Prophylactic Effect of Bovine Colostrum on Intestinal Barrier Function in Zonulin Transgenic Mice: A Transcriptomic Study.

The intestinal barrier comprises a single layer of epithelial cells tightly joined to form a physical barrier. Disruption or compromise of the intestinal barrier can lead to the inadvertent activation of immune cells, potentially causing an increased risk of chronic inflammation in various tissues. Recent research has suggested that specific dietary components may influence the function of the intestinal barrier, potentially offering a means to prevent or mitigate inflammatory disorders. However, the precise mechanism underlying these effects remains unclear. Bovine colostrum (BC), the first milk from cows after calving, is a natural source of nutrients with immunomodulatory, anti-inflammatory, and gut-barrier fortifying properties. This novel study sought to investigate the transcriptome in BC-treated Zonulin transgenic mice (Ztm), characterized by dysbiotic microbiota, intestinal hyperpermeability, and mild hyperactivity, applying RNA sequencing. Seventy-five tissue samples from the duodenum, colon, and brain of Ztm and wild-type (WT) mice were dissected, processed, and RNA sequenced. The expression profiles were analyzed and integrated to identify differentially expressed genes (DEGs) and differentially expressed transcripts (DETs). These were then further examined using bioinformatics tools. RNA-seq analysis identified 1298 DEGs and 20,952 DETs in the paired (Ztm treatment vs. Ztm control) and reference (WT controls) groups. Of these, 733 DEGs and 10,476 DETs were upregulated, while 565 DEGs and 6097 DETs were downregulated. BC-treated Ztm female mice showed significant upregulation of cingulin (Cgn) and claudin 12 (Cldn12) duodenum and protein interactions, as well as molecular pathways and interactions pertaining to tight junctions, while BC-treated Ztm males displayed an upregulation of transcripts like occludin (Ocln) and Rho/Rac guanine nucleotide exchange factor 2 (Arhgf2) and cellular structures and interfaces, protein-protein interactions, and organization and response mechanisms. This comprehensive analysis reveals the influence of BC treatment on tight junctions (TJs) and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) signaling pathway gene expressions. The present study is the first to analyze intestinal and brain samples from BC-treated Ztm mice applying high-throughput RNA sequencing. This study revealed molecular interaction in intestinal barrier function and identified hub genes and their functional pathways and biological processes in response to BC treatment in Ztm mice. Further research is needed to validate these findings and explore their implications for dietary interventions aimed at improving intestinal barrier integrity and function. The MGH Institutional Animal Care and Use Committee authorized the animal study (2013N000013).

Fully First-Principles Surface Spectroscopy with Machine Learning.

Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.

Maternal helminth infection protects offspring from high-fat-diet-induced obesity through altered microbiota and SCFAs.

Helminth-induced Th2 immunity and gut microbiota have been recently shown to be highly effective in modulating metabolic syndromes in animal models. This study aimed to determine whether maternal immunity and microbial factors affect the induction and development of obesity in offspring. Here, Heligomosomoides polygyrus (Hp)-infected or control female C57BL/6J mice mated with normal males and their offspring were fed a high-fat diet (HFD) for 9 weeks after weaning. Our results showed that Hp-induced maternal outcomes during gestation and lactation significantly impacted offspring metabolic phenotypes. This was evidenced by results showing that offspring from helminth-infected mothers on an HFD (Hp-offspring + HFD) gained significantly less body weight than those from uninfected mothers (Cont-offspring + HFD). Hp-offspring + HFD exhibited no Th2 phenotype but displayed a pattern of gut microbiota composition similar to that of Hp-infected mothers. Cross-fostering experiments confirmed that the helminth-induced maternal attenuation of offspring obesity was mediated through both prenatal and postnatal effects. Our results further showed that helminth-infected dams and their offspring had a markedly altered gut microbiome composition, with increased production of short-chain fatty acids (SCFAs). Intriguingly, Hp-infected mothers and Hp-offspring + HFD showed increased SCFA receptor (GPR) expression in adipose and colonic tissues compared to noninfected mothers and Cont-offspring + HFD, respectively. Moreover, SCFA supplementation to the pups of uninfected control mothers during lactation protected against HFD-induced weight gain, which corresponded with changes in gut bacterial colonization. Collectively, our findings provide new insights into the complex interaction of maternal immune status and gut microbiome, Hp infection, and the immunity and gut microbiome in obese-prone offspring in infant life.

Temperature Dependent Properties of the Aqueous Electron.

The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.

In situ spectroelectrochemical probing of CO redox landscape on copper single-crystal surfaces.

Electrochemical reduction of CO(2) to value-added chemicals and fuels is a promising strategy to sustain pressing renewable energy demands and to address climate change issues. Direct observation of reaction intermediates during the CO(2) reduction reaction will contribute to mechanistic understandings and thus promote the design of catalysts with the desired activity, selectivity, and stability. Herein, we combined in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy and ab initio molecular dynamics calculations to investigate the CORR process on Cu single-crystal surfaces in various electrolytes. Competing redox pathways and coexistent intermediates of CO adsorption (*COatop and *CObridge), dimerization (protonated dimer *HOCCOH and its dehydrated *CCO), oxidation (*CO2- and *CO32-), and hydrogenation (*CHO), as well as Cu-Oad/Cu-OHad species at Cu-electrolyte interfaces, were simultaneously identified using in situ spectroscopy and further confirmed with isotope-labeling experiments. With AIMD simulations, we report accurate vibrational frequency assignments of these intermediates based on the calculated vibrational density of states and reveal the corresponding species in the electrochemical CO redox landscape on Cu surfaces. Our findings provide direct insights into key intermediates during the CO(2)RR and offer a full-spectroscopic tool (40-4,000 cm-1) for future mechanistic studies.

Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks.

Accurate and efficient simulation of liquids, such as water and salt solutions, using high-level wave function theories is still a formidable task for computational chemists owing to the high computational costs. In this study, we develop a deep machine learning potential based on fragment-based second-order Møller-Plesset perturbation theory (DP-MP2) for water through neural networks. We show that the DP-MP2 potential predicts the structural, dynamical, and thermodynamic properties of liquid water in better agreement with the experimental data than previous studies based on density functional theory (DFT). The nuclear quantum effects (NQEs) on the properties of liquid water are also examined, which are noticeable in affecting the structural and dynamical properties of liquid water under ambient conditions. This work provides a general framework for quantitative predictions of the properties of condensed-phase systems with the accuracy of high-level wave function theory while achieving significant computational savings compared to ab initio simulations.

Shallow and deep trap states of solvated electrons in methanol and their formation, electronic excitation, and relaxation dynamics.

We present condensed-phase first-principles molecular dynamics simulations to elucidate the presence of different electron trapping sites in liquid methanol and their roles in the formation, electronic transitions, and relaxation of solvated electrons (emet -) in methanol. Excess electrons injected into liquid methanol are most likely trapped by methyl groups, but rapidly diffuse to more stable trapping sites with dangling OH bonds. After localization at the sites with one free OH bond (1OH trapping sites), reorientation of other methanol molecules increases the OH coordination number and the trap depth, and ultimately four OH bonds become coordinated with the excess electrons under thermal conditions. The simulation identified four distinct trapping states with different OH coordination numbers. The simulation results also revealed that electronic transitions of emet - are primarily due to charge transfer between electron trapping sites (cavities) formed by OH and methyl groups, and that these transitions differ from hydrogenic electronic transitions involving aqueous solvated electrons (eaq -). Such charge transfer also explains the alkyl-chain-length dependence of the photoabsorption peak wavelength and the excited-state lifetime of solvated electrons in primary alcohols.

Dynamics and control of active sites in hierarchically nanostructured cobalt phosphide/chalcogenide-based electrocatalysts for water splitting.

The rational design of efficient electrocatalysts for industrial water splitting is essential to generate sustainable hydrogen fuel. However, a comprehensive understanding of the complex catalytic mechanisms under harsh reaction conditions remains a major challenge. We apply a self-templated strategy to introduce hierarchically nanostructured "all-surface" Fe-doped cobalt phosphide nanoboxes (Co@CoFe-P NBs) as alternative electrocatalysts for industrial-scale applications. Operando Raman spectroscopy and X-ray absorption spectroscopy (XAS) experiments were carried out to track the dynamics of their structural reconstruction and the real catalytically active intermediates during water splitting. Our operando analyses reveal that partial Fe substitution in cobalt phosphides promotes a structural reconstruction into P-Co-O-Fe-P configurations with low-valence metal centers (M0/M+) during the hydrogen evolution reaction (HER). Results from density functional theory (DFT) demonstrate that these in situ reconstructed configurations significantly enhance the HER performance by lowering the energy barrier for water dissociation and by facilitating the adsorption/desorption of HER intermediates (H*). The competitive activity in the oxygen evolution reaction (OER) arises from the transformation of the reconstructed P-Co-O-Fe-P configurations into oxygen-bridged, high-valence CoIV-O-FeIV moieties as true active intermediates. In sharp contrast, the formation of such CoIII/IV-O-FeIII/IV moieties in Co-FeOOH is hindered under the same conditions, which outlines the key advantages of phosphide-based electrocatalysts. Ex situ studies of the as-synthesized reference cobalt sulfides (Co-S), Fe doped cobalt selenides (Co@CoFe-Se), and Fe doped cobalt tellurides (Co@CoFe-Te) further corroborate the observed structural transformations. These insights are vital to systematically exploit the intrinsic catalytic mechanisms of non-oxide, low-cost, and robust overall water splitting electrocatalysts for future energy conversion and storage.

Nanoscale Chemical Imaging of Coadsorbed Thiolate Self-Assembled Monolayers on Au(111) by Tip-Enhanced Raman Spectroscopy.

Self-assembled monolayers (SAMs) of thiolates on metal surfaces are of key importance for engineering surfaces with tunable properties. However, it remains challenging to understand binary thiolate SAMs on metals at the nanoscale under ambient conditions. Here, we employ tip-enhanced Raman spectroscopy (TERS) and density functional theory (DFT) calculations to investigate the local information of binary SAMs on Au(111) coadsorbed from an equimolar mixture of p-cyanobenzenethiol (pCTP) and p-aminothiophenol (pATP), including chemical composition, coadsorption behavior, phase segregation, plasmon-induced photocatalysis, and solvation effects. We found that upon competitive adsorption of pCTP and pATP on Au(111) from a methanolic solution, the coadsorption initially occurs randomly and homogeneously; eventually, pATP is replaced by pCTP through the gradual growth of pCTP nanodomains. TERS imaging also allows for visualization of the plasmon-induced coupling of pATP to p,p'-dimercaptoazobenzene (DMAB) and the solvation-induced phase segregation of the binary SAMs into nanodomains, with a spatial resolution of ∼9 nm under ambient conditions. According to DFT calculations, these aromatic thiolates differing only in their functional groups, -CN versus -NH2, show different adsorption energies on Au(111) in vacuum and methanol, and thus, the solvation effect on the adsorption energy of these thiolates in methanol can determine the dispersion state and replacement order of the binary thiolates on Au(111).

Prophylactic Effect of Bovine Colostrum on Intestinal Microbiota and Behavior in Wild-Type and Zonulin Transgenic Mice.

The microbiota-gut-brain axis (MGBA) involves bidirectional communication between intestinal microbiota and the gastrointestinal (GI) tract, central nervous system (CNS), neuroendocrine/neuroimmune systems, hypothalamic-pituitary-adrenal (HPA) axis, and enteric nervous system (ENS). The intestinal microbiota can influence host physiology and pathology. Dysbiosis involves the loss of beneficial microbial input or signal, diversity, and expansion of pathobionts, which can lead to loss of barrier function and increased intestinal permeability (IP). Colostrum, the first milk from mammals after birth, is a natural source of nutrients and is rich in oligosaccharides, immunoglobulins, growth factors, and anti-microbial components. The aim of this study was to investigate if bovine colostrum (BC) administration might modulate intestinal microbiota and, in turn, behavior in two mouse models, wild-type (WT) and Zonulin transgenic (Ztm)-the latter of which is characterized by dysbiotic microbiota, increased intestinal permeability, and mild hyperactivity-and to compare with control mice. Bioinformatics analysis of the microbiome showed that consumption of BC was associated with increased taxonomy abundance (p = 0.001) and diversity (p = 0.004) of potentially beneficial species in WT mice and shifted dysbiotic microbial community towards eubiosis in Ztm mice (p = 0.001). BC induced an anxiolytic effect in WT female mice compared with WT female control mice (p = 0.0003), and it reduced anxiogenic behavior in Ztm female mice compared with WT female control mice (p = 0.001), as well as in Ztm male mice compared with WT BC male mice (p = 0.03). As evidenced in MGBA interactions, BC supplementation may well be applied for prophylactic approaches in the future. Further research is needed to explore human interdependencies between intestinal microbiota, including eubiosis and pathobionts, and neuroinflammation, and the potential value of BC for human use. The MGH Institutional Animal Care and Use Committee authorized the animal study (2013N000013).

Novel role of zonulin in the pathophysiology of gastro-duodenal transit: a clinical and translational study.

We examined the relationship between zonulin and gastric motility in critical care patients and a translational mouse model of systemic inflammation. Gastric motility and haptoglobin (HP) 2 isoform quantification, proxy for zonulin, were examined in patients. Inflammation was triggered by lipopolysaccharide (LPS) injection in C57Bl/6 zonulin transgenic mouse (Ztm) and wildtype (WT) mice as controls, and gastro-duodenal transit was examined by fluorescein-isothiocyanate, 6 and 12 h after LPS-injection. Serum cytokines and zonulin protein levels, and zonulin gastric-duodenal mRNA expression were examined. Eight of 20 patients [14 years, IQR (12.25, 18)] developed gastric dysmotility and were HP2 isoform-producing. HP2 correlated with gastric dysmotility (r = - 0.51, CI - 0.81 to 0.003, p = 0.048). LPS injection induced a time-dependent increase in IL-6 and KC-Gro levels in all mice (p < 0.0001). Gastric dysmotility was reduced similarly in Ztm and WT mice in a time-dependent manner. Ztm had 16% faster duodenal motility than WT mice 6H post-LPS, p = 0.01. Zonulin mRNA expression by delta cycle threshold (dCT) was higher in the stomach (9.7, SD 1.4) than the duodenum (13.9, SD 1.4) 6H post-LPS, p = 0.04. Serum zonulin protein levels were higher in LPS-injected mice compared to vehicle-injected animals in a time-dependent manner. Zonulin correlated with gastric dysmotility in patients. A mouse model had time-dependent gastro-duodenal dysmotility after LPS-injection that paralleled zonulin mRNA expression and protein levels.

Hyaluronan-induced alterations of the gut microbiome protects mice against Citrobacter rodentium infection and intestinal inflammation.

Hyaluronan is a glycosaminoglycan polymer that has been shown to play an important role in homeostasis of the gastrointestinal tract. However, its mechanistic significance in gastrointestinal epithelial barrier elements remain unexplored. Here, our results revealed that hyaluronan treatment resulted in significant changes in the gut microbiota in mice. To demonstrate the functional consequences of hyaluronan-treatment and hyaluronan-induced microbiota alterations, Citrobacter rodentium- and DSS-induced colitis models and microbiota transplantation approaches were utilized. We showed that hyaluronan alleviated intestinal inflammation in both pathogen and chemically induced intestinal mucosal damage. The protection in bacterial colitis was associated with enhanced C. rodentium clearance and alleviation of pathogen-induced gut dysbiosis. Microbiota transplantation experiments showed that the hyaluronan-altered microbiota is sufficient to confer protection against C. rodentium infection. Colonization with Akkermansia muciniphila, a commensal bacterium that is greatly enriched by hyaluronan treatment, alleviated C. rodentium-induced bacterial colitis in mice. Additionally, Akkermansia-induced protection was found to be associated with the induction of goblet cells and the production of mucins and epithelial antimicrobial peptides. Collectively, these results provide novel insights into the regulatory role of hyaluronan in modulating the gut microbiota and immunity in enteric infection and inflammation, with therapeutic potential for gut microbiome-targeted immunotherapy.

Modeling Electrified Pt(111)-Had/Water Interfaces from Ab Initio Molecular Dynamics.

Unraveling the atomistic structures of electric double layers (EDL) at electrified interfaces is of paramount importance for understanding the mechanisms of electrocatalytic reactions and rationally designing electrode materials with better performance. Despite numerous efforts dedicated in the past, a molecular level understanding of the EDL is still lacking. Combining the state-of-the-art ab initio molecular dynamics (AIMD) and recently developed computational standard hydrogen electrode (cSHE) method, it is possible to realistically simulate the EDL under well-defined electrochemical conditions. In this work, we report extensive AIMD calculation of the electrified Pt(111)-Had/water interfaces at the saturation coverage of adsorbed hydrogen (Had) corresponding to the typical hydrogen evolution reaction conditions. We calculate the electrode potentials of a series of EDL models with various surface charge densities using the cSHE method and further obtain the Helmholtz capacitance that agrees with experiment. Furthermore, the AIMD simulations allow for detailed structural analyses of the electrified interfaces, such as the distribution of adsorbate Had and the structures of interface water and counterions, which can in turn explain the computed dielectric property of interface water. Our calculation provides valuable molecular insight into the electrified interfaces and a solid basis for understanding a variety of electrochemical processes occurring inside the EDL.

Efficient Quantum Vibrational Spectroscopy of Water with High-Order Path Integrals: From Bulk to Interfaces.

Vibrational spectroscopy is key in probing the interplay between the structure and dynamics of aqueous systems. To map different regions of experimental spectra to the microscopic structure of a system, it is important to combine them with first-principles atomistic simulations that incorporate the quantum nature of nuclei. Here we show that the large cost of calculating the quantum vibrational spectra of aqueous systems can be dramatically reduced compared with standard path integral methods by using approximate quantum dynamics based on high-order path integrals. Together with state-of-the-art machine-learned electronic properties, our approach gives an excellent description not only of the infrared and Raman spectra of bulk water but also of the 2D correlation and the more challenging sum-frequency generation spectra of the water-air interface. This paves the way for understanding complex interfaces such as water encapsulated between or in contact with hydrophobic and hydrophilic materials through robust and inexpensive surface-sensitive and multidimensional spectra with first-principles accuracy.

In-situ nanospectroscopic imaging of plasmon-induced two-dimensional [4+4]-cycloaddition polymerization on Au(111).

Plasmon-induced chemical reactions (PICRs) have recently become promising approaches for highly efficient light-chemical energy conversion. However, an in-depth understanding of their mechanisms at the nanoscale still remains challenging. Here, we present an in-situ investigation by tip-enhanced Raman spectroscopy (TERS) imaging of the plasmon-induced [4+4]-cycloaddition polymerization within anthracene-based monomer monolayers physisorbed on Au(111), and complement the experimental results with density functional theory (DFT) calculations. This two-dimensional (2D) polymerization can be flexibly triggered and manipulated by the hot carriers, and be monitored simultaneously by TERS in real time and space. TERS imaging provides direct evidence for covalent bond formation with ca. 3.7 nm spatial resolution under ambient conditions. Combined with DFT calculations, the TERS results demonstrate that the lateral polymerization on Au(111) occurs by a hot electron tunneling mechanism, and crosslinks form via a self-stimulating growth mechanism. We show that TERS is promising to be plasmon-induced nanolithography for organic 2D materials.

CO2 adsorption on the pristine and reduced CeO2 (111) surface: Geometries and vibrational spectra by first principles simulations.

First principles simulations of carbon dioxide adsorbed on the ceria (CeO2) (111) surface are discussed in terms of structural features, stability, charge transfer, and vibrational modes. For this purpose, different density functional theory methods, such as Perdew-Burke-Ernzerhof (PBE) PBE and Hubbard correction, hybrid functionals, and different basis sets have been applied and compared. Both the stoichiometric and the reduced (111) surfaces are considered, where the electronic structure of the latter is obtained by introducing oxygen vacancies on the topmost or the subsurface oxygen layer. Both the potential energy surfaces of the reduced ceria surface and the adsorbate-surface complex are characterized by numerous local minima, of which the relative stability depends strongly on the electronic structure method of choice. Bent CO2 configurations in close vicinity to the surface oxygen vacancy that partially re-oxidize the reduced ceria surface have been identified as the most probable stable minima. However, the oxygen vacancy concentration on the surface turns out to have a direct impact on the relative stability of possible adsorption configurations. Finally, the vibrational analyses of selected adsorbed species on both the stoichiometric and reduced surfaces show promising agreement with previous theoretical and experimental results.

Simulating the ghost: quantum dynamics of the solvated electron.

The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.